This project supports research to improve methods for estimating absolute and attributable risk and collaborative studies to estimate such risks for various cancers. We analyzed data from the Breast Cancer Detection and Demonstration on Project (BCDDP). The intraclass correlation over time was above 0.9, indicating that this strong correlate of breast cancer risk may be useful for predicting risk. The data needed to incorporate mammographic density into models for predicting risk have been assembled. We began a collaboration to develop a model for the individualized absolute risk of colon cancer. We have constructed relative risk models for proximol and distal disease, based on case-control data. We will use population incidence rates to estimte absolute risk of the earlier of proximal or distal colon cancer. We gathered data to project the individualized risk of the earlier of ovarian and breast cancer, as well as data for projecting the individualized risk of melanoma. We analyzed the strengths and weaknesses of the kin-cohort design for estimating the risk of disease from an autosomal dominant gene. We developed marginal methods of analysis of kin-cohort data that are more robust to such residual familial correlation than are likelihood-based methods that ignore such correlation. We developed maximum likelihood and pseudo-likelihood methods to estimate monotone genotype-specific survival functions from kin-cohort data. We developed a bivariate copula model to accommodate residual familial correlation and to project risk based not only on genotype but also on the phenotypes of other family members. We developed bivariate cure models for familial survival data from randomly selected families. We developed methods for estimating covariate-adjusted survival curves and population attributable risk from stratified case-cohort studies. We developed a graphical method for assessing the predictive power of a set of covariates.